Overview

Dataset statistics

Number of variables23
Number of observations1687861
Missing cells100915
Missing cells (%)0.3%
Duplicate rows168735
Duplicate rows (%)10.0%
Total size in memory296.2 MiB
Average record size in memory184.0 B

Variable types

Unsupported1
Numeric15
Boolean7

Alerts

Dataset has 168735 (10.0%) duplicate rowsDuplicates
national_inv is highly overall correlated with sales_1_month and 4 other fieldsHigh correlation
in_transit_qty is highly overall correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly overall correlated with in_transit_qty and 6 other fieldsHigh correlation
forecast_6_month is highly overall correlated with in_transit_qty and 6 other fieldsHigh correlation
forecast_9_month is highly overall correlated with in_transit_qty and 6 other fieldsHigh correlation
sales_1_month is highly overall correlated with national_inv and 8 other fieldsHigh correlation
sales_3_month is highly overall correlated with national_inv and 8 other fieldsHigh correlation
sales_6_month is highly overall correlated with national_inv and 8 other fieldsHigh correlation
sales_9_month is highly overall correlated with national_inv and 8 other fieldsHigh correlation
min_bank is highly overall correlated with national_inv and 5 other fieldsHigh correlation
perf_6_month_avg is highly overall correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly overall correlated with perf_6_month_avgHigh correlation
potential_issue is highly imbalanced (99.3%)Imbalance
oe_constraint is highly imbalanced (99.8%)Imbalance
stop_auto_buy is highly imbalanced (77.5%)Imbalance
rev_stop is highly imbalanced (99.5%)Imbalance
went_on_backorder is highly imbalanced (94.2%)Imbalance
lead_time has 100894 (6.0%) missing valuesMissing
national_inv is highly skewed (γ1 = 340.2858003)Skewed
in_transit_qty is highly skewed (γ1 = 166.1834042)Skewed
forecast_3_month is highly skewed (γ1 = 138.9683252)Skewed
forecast_6_month is highly skewed (γ1 = 138.9614272)Skewed
forecast_9_month is highly skewed (γ1 = 143.2988747)Skewed
sales_1_month is highly skewed (γ1 = 196.1199899)Skewed
sales_3_month is highly skewed (γ1 = 141.2863795)Skewed
sales_6_month is highly skewed (γ1 = 139.176712)Skewed
sales_9_month is highly skewed (γ1 = 135.0541915)Skewed
min_bank is highly skewed (γ1 = 131.2126489)Skewed
pieces_past_due is highly skewed (γ1 = 412.3919004)Skewed
local_bo_qty is highly skewed (γ1 = 165.1905479)Skewed
sku is an unsupported type, check if it needs cleaning or further analysisUnsupported
national_inv has 108425 (6.4%) zerosZeros
in_transit_qty has 1344662 (79.7%) zerosZeros
forecast_3_month has 1177722 (69.8%) zerosZeros
forecast_6_month has 1084111 (64.2%) zerosZeros
forecast_9_month has 1033241 (61.2%) zerosZeros
sales_1_month has 959817 (56.9%) zerosZeros
sales_3_month has 759225 (45.0%) zerosZeros
sales_6_month has 647038 (38.3%) zerosZeros
sales_9_month has 585994 (34.7%) zerosZeros
min_bank has 872331 (51.7%) zerosZeros
pieces_past_due has 1662571 (98.5%) zerosZeros
perf_6_month_avg has 39013 (2.3%) zerosZeros
perf_12_month_avg has 32975 (2.0%) zerosZeros
local_bo_qty has 1664518 (98.6%) zerosZeros

Reproduction

Analysis started2023-06-12 13:14:56.065631
Analysis finished2023-06-12 13:24:26.154579
Duration9 minutes and 30.09 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

sku
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size12.9 MiB

national_inv
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct14969
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean496.11178
Minimum-27256
Maximum12334404
Zeros108425
Zeros (%)6.4%
Negative5888
Negative (%)0.3%
Memory size12.9 MiB
2023-06-12T18:54:26.690309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-27256
5-th percentile0
Q14
median15
Q380
95-th percentile922
Maximum12334404
Range12361660
Interquartile range (IQR)76

Descriptive statistics

Standard deviation29615.234
Coefficient of variation (CV)59.69468
Kurtosis131276.59
Mean496.11178
Median Absolute Deviation (MAD)13
Skewness340.2858
Sum8.3736723 × 108
Variance8.7706207 × 108
MonotonicityNot monotonic
2023-06-12T18:54:27.170080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108425
 
6.4%
2 107869
 
6.4%
3 90577
 
5.4%
4 69588
 
4.1%
5 58916
 
3.5%
1 58829
 
3.5%
6 50832
 
3.0%
7 46399
 
2.7%
10 46277
 
2.7%
8 40147
 
2.4%
Other values (14959) 1010001
59.8%
ValueCountFrequency (%)
-27256 1
< 0.1%
-25414 2
< 0.1%
-22154 2
< 0.1%
-17698 1
< 0.1%
-17669 1
< 0.1%
-13491 1
< 0.1%
-9925 1
< 0.1%
-8230 1
< 0.1%
-8170 1
< 0.1%
-8130 1
< 0.1%
ValueCountFrequency (%)
12334404 1
< 0.1%
12324456 1
< 0.1%
12315072 1
< 0.1%
12309096 1
< 0.1%
12285100 1
< 0.1%
12181612 1
< 0.1%
12166440 1
< 0.1%
6363276 1
< 0.1%
6352932 1
< 0.1%
6335300 1
< 0.1%

lead_time
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing100894
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean7.872267
Minimum0
Maximum52
Zeros10511
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:27.587285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median8
Q39
95-th percentile12
Maximum52
Range52
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.056024
Coefficient of variation (CV)0.89631411
Kurtosis26.237228
Mean7.872267
Median Absolute Deviation (MAD)1
Skewness4.5562954
Sum12493028
Variance49.787475
MonotonicityNot monotonic
2023-06-12T18:54:28.815696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8 682186
40.4%
2 337402
20.0%
12 199700
 
11.8%
4 128537
 
7.6%
9 123649
 
7.3%
52 30113
 
1.8%
3 16253
 
1.0%
10 14192
 
0.8%
0 10511
 
0.6%
14 10314
 
0.6%
Other values (22) 34110
 
2.0%
(Missing) 100894
 
6.0%
ValueCountFrequency (%)
0 10511
 
0.6%
1 21
 
< 0.1%
2 337402
20.0%
3 16253
 
1.0%
4 128537
 
7.6%
5 4031
 
0.2%
6 5365
 
0.3%
7 209
 
< 0.1%
8 682186
40.4%
9 123649
 
7.3%
ValueCountFrequency (%)
52 30113
1.8%
40 48
 
< 0.1%
35 35
 
< 0.1%
30 312
 
< 0.1%
28 84
 
< 0.1%
26 105
 
< 0.1%
25 7
 
< 0.1%
24 115
 
< 0.1%
23 14
 
< 0.1%
22 133
 
< 0.1%

in_transit_qty
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5230
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean44.052022
Minimum0
Maximum489408
Zeros1344662
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:29.239329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile68
Maximum489408
Range489408
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1342.7417
Coefficient of variation (CV)30.480819
Kurtosis39606.104
Mean44.052022
Median Absolute Deviation (MAD)0
Skewness166.1834
Sum74353646
Variance1802955.4
MonotonicityNot monotonic
2023-06-12T18:54:29.696714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344662
79.7%
1 36515
 
2.2%
2 22236
 
1.3%
3 17189
 
1.0%
4 15364
 
0.9%
5 13003
 
0.8%
6 11384
 
0.7%
10 8964
 
0.5%
8 8822
 
0.5%
7 8131
 
0.5%
Other values (5220) 201590
 
11.9%
ValueCountFrequency (%)
0 1344662
79.7%
1 36515
 
2.2%
2 22236
 
1.3%
3 17189
 
1.0%
4 15364
 
0.9%
5 13003
 
0.8%
6 11384
 
0.7%
7 8131
 
0.5%
8 8822
 
0.5%
9 6285
 
0.4%
ValueCountFrequency (%)
489408 1
< 0.1%
487680 1
< 0.1%
328060 1
< 0.1%
327156 1
< 0.1%
292093 1
< 0.1%
288960 1
< 0.1%
288768 1
< 0.1%
285365 1
< 0.1%
276703 1
< 0.1%
254688 1
< 0.1%

forecast_3_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7825
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean178.11928
Minimum0
Maximum1427612
Zeros1177722
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:30.106455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile300
Maximum1427612
Range1427612
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5026.5531
Coefficient of variation (CV)28.220151
Kurtosis25637.55
Mean178.11928
Median Absolute Deviation (MAD)0
Skewness138.96833
Sum3.0064041 × 108
Variance25266236
MonotonicityNot monotonic
2023-06-12T18:54:30.496801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1177722
69.8%
1 31452
 
1.9%
2 26612
 
1.6%
5 19740
 
1.2%
4 18563
 
1.1%
3 16194
 
1.0%
10 14370
 
0.9%
6 13612
 
0.8%
12 11031
 
0.7%
20 10946
 
0.6%
Other values (7815) 347618
 
20.6%
ValueCountFrequency (%)
0 1177722
69.8%
1 31452
 
1.9%
2 26612
 
1.6%
3 16194
 
1.0%
4 18563
 
1.1%
5 19740
 
1.2%
6 13612
 
0.8%
7 8152
 
0.5%
8 10357
 
0.6%
9 6880
 
0.4%
ValueCountFrequency (%)
1427612 1
< 0.1%
1218328 2
< 0.1%
1126656 1
< 0.1%
1103084 1
< 0.1%
1058396 1
< 0.1%
1046592 1
< 0.1%
1037228 1
< 0.1%
1013708 1
< 0.1%
992540 1
< 0.1%
980780 1
< 0.1%

forecast_6_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct11114
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean344.98666
Minimum0
Maximum2461360
Zeros1084111
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:30.945109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile600
Maximum2461360
Range2461360
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9795.1519
Coefficient of variation (CV)28.392842
Kurtosis25189.904
Mean344.98666
Median Absolute Deviation (MAD)0
Skewness138.96143
Sum5.8228919 × 108
Variance95945000
MonotonicityNot monotonic
2023-06-12T18:54:31.371773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1084111
64.2%
1 27857
 
1.7%
2 26492
 
1.6%
3 19975
 
1.2%
4 18782
 
1.1%
5 16649
 
1.0%
6 16113
 
1.0%
10 15810
 
0.9%
8 12976
 
0.8%
20 11981
 
0.7%
Other values (11104) 437114
25.9%
ValueCountFrequency (%)
0 1084111
64.2%
1 27857
 
1.7%
2 26492
 
1.6%
3 19975
 
1.2%
4 18782
 
1.1%
5 16649
 
1.0%
6 16113
 
1.0%
7 11512
 
0.7%
8 12976
 
0.8%
9 6761
 
0.4%
ValueCountFrequency (%)
2461360 1
< 0.1%
2446072 1
< 0.1%
2119148 1
< 0.1%
2109740 1
< 0.1%
2104128 1
< 0.1%
2094336 1
< 0.1%
2087396 1
< 0.1%
2085044 1
< 0.1%
2056820 1
< 0.1%
2036828 1
< 0.1%

forecast_9_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13662
Distinct (%)0.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean506.36443
Minimum0
Maximum3777304
Zeros1033241
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:31.779727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile896
Maximum3777304
Range3777304
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14378.924
Coefficient of variation (CV)28.396393
Kurtosis27048.452
Mean506.36443
Median Absolute Deviation (MAD)0
Skewness143.29887
Sum8.5467227 × 108
Variance2.0675344 × 108
MonotonicityNot monotonic
2023-06-12T18:54:32.146949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1033241
61.2%
1 26781
 
1.6%
2 25662
 
1.5%
3 20716
 
1.2%
4 19322
 
1.1%
5 17495
 
1.0%
6 16611
 
1.0%
10 16153
 
1.0%
8 13953
 
0.8%
12 12425
 
0.7%
Other values (13652) 485501
28.8%
ValueCountFrequency (%)
0 1033241
61.2%
1 26781
 
1.6%
2 25662
 
1.5%
3 20716
 
1.2%
4 19322
 
1.1%
5 17495
 
1.0%
6 16611
 
1.0%
7 10402
 
0.6%
8 13953
 
0.8%
9 8012
 
0.5%
ValueCountFrequency (%)
3777304 1
< 0.1%
3760840 1
< 0.1%
3232820 1
< 0.1%
3229292 1
< 0.1%
3206948 1
< 0.1%
3196364 1
< 0.1%
3158732 1
< 0.1%
3149324 1
< 0.1%
3103460 1
< 0.1%
3062016 1
< 0.1%

sales_1_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5764
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean55.926069
Minimum0
Maximum741774
Zeros959817
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:32.539987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile107
Maximum741774
Range741774
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1928.1959
Coefficient of variation (CV)34.477587
Kurtosis53855.926
Mean55.926069
Median Absolute Deviation (MAD)0
Skewness196.11999
Sum94395374
Variance3717939.3
MonotonicityNot monotonic
2023-06-12T18:54:32.940927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 959817
56.9%
1 144881
 
8.6%
2 78759
 
4.7%
3 49410
 
2.9%
4 37905
 
2.2%
5 29720
 
1.8%
6 23695
 
1.4%
7 18997
 
1.1%
8 17003
 
1.0%
10 14605
 
0.9%
Other values (5754) 313068
 
18.5%
ValueCountFrequency (%)
0 959817
56.9%
1 144881
 
8.6%
2 78759
 
4.7%
3 49410
 
2.9%
4 37905
 
2.2%
5 29720
 
1.8%
6 23695
 
1.4%
7 18997
 
1.1%
8 17003
 
1.0%
9 13983
 
0.8%
ValueCountFrequency (%)
741774 1
< 0.1%
741762 1
< 0.1%
741750 1
< 0.1%
393665 1
< 0.1%
376025 1
< 0.1%
369425 1
< 0.1%
366191 1
< 0.1%
361803 1
< 0.1%
361239 1
< 0.1%
359505 1
< 0.1%

sales_3_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct10495
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean175.02593
Minimum0
Maximum1105478
Zeros759225
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:33.363125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile350
Maximum1105478
Range1105478
Interquartile range (IQR)15

Descriptive statistics

Standard deviation5192.3776
Coefficient of variation (CV)29.666334
Kurtosis24198.861
Mean175.02593
Median Absolute Deviation (MAD)1
Skewness141.28638
Sum2.9541927 × 108
Variance26960785
MonotonicityNot monotonic
2023-06-12T18:54:33.787251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 759225
45.0%
1 136966
 
8.1%
2 81289
 
4.8%
3 53082
 
3.1%
4 41533
 
2.5%
5 34128
 
2.0%
6 27829
 
1.6%
7 23243
 
1.4%
8 20904
 
1.2%
10 17873
 
1.1%
Other values (10485) 491788
29.1%
ValueCountFrequency (%)
0 759225
45.0%
1 136966
 
8.1%
2 81289
 
4.8%
3 53082
 
3.1%
4 41533
 
2.5%
5 34128
 
2.0%
6 27829
 
1.6%
7 23243
 
1.4%
8 20904
 
1.2%
9 17571
 
1.0%
ValueCountFrequency (%)
1105478 1
< 0.1%
1104181 1
< 0.1%
1100523 1
< 0.1%
1094112 1
< 0.1%
1091281 1
< 0.1%
1084974 1
< 0.1%
1081560 1
< 0.1%
1076623 1
< 0.1%
1070623 1
< 0.1%
1062979 1
< 0.1%

sales_6_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct14818
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean341.72884
Minimum0
Maximum2146625
Zeros647038
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:34.202253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q331
95-th percentile696
Maximum2146625
Range2146625
Interquartile range (IQR)31

Descriptive statistics

Standard deviation9613.1671
Coefficient of variation (CV)28.13098
Kurtosis24305.445
Mean341.72884
Median Absolute Deviation (MAD)2
Skewness139.17671
Sum5.7679044 × 108
Variance92412982
MonotonicityNot monotonic
2023-06-12T18:54:34.622688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 647038
38.3%
1 127076
 
7.5%
2 79418
 
4.7%
3 52987
 
3.1%
4 41716
 
2.5%
5 34108
 
2.0%
6 28319
 
1.7%
7 23244
 
1.4%
8 21102
 
1.3%
9 18383
 
1.1%
Other values (14808) 614469
36.4%
ValueCountFrequency (%)
0 647038
38.3%
1 127076
 
7.5%
2 79418
 
4.7%
3 52987
 
3.1%
4 41716
 
2.5%
5 34108
 
2.0%
6 28319
 
1.7%
7 23244
 
1.4%
8 21102
 
1.3%
9 18383
 
1.1%
ValueCountFrequency (%)
2146625 1
< 0.1%
2145715 1
< 0.1%
2133557 1
< 0.1%
2123946 1
< 0.1%
2117803 1
< 0.1%
2113901 1
< 0.1%
2112231 1
< 0.1%
2098852 1
< 0.1%
2086531 1
< 0.1%
1799099 1
< 0.1%

sales_9_month
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct18341
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean525.2697
Minimum0
Maximum3205172
Zeros585994
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:35.004906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q347
95-th percentile1060
Maximum3205172
Range3205172
Interquartile range (IQR)47

Descriptive statistics

Standard deviation14838.614
Coefficient of variation (CV)28.249514
Kurtosis22844.806
Mean525.2697
Median Absolute Deviation (MAD)4
Skewness135.05419
Sum8.8658172 × 108
Variance2.2018445 × 108
MonotonicityNot monotonic
2023-06-12T18:54:35.371421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 585994
34.7%
1 120294
 
7.1%
2 76521
 
4.5%
3 52294
 
3.1%
4 41399
 
2.5%
5 34323
 
2.0%
6 28784
 
1.7%
7 23545
 
1.4%
8 21115
 
1.3%
9 18744
 
1.1%
Other values (18331) 684847
40.6%
ValueCountFrequency (%)
0 585994
34.7%
1 120294
 
7.1%
2 76521
 
4.5%
3 52294
 
3.1%
4 41399
 
2.5%
5 34323
 
2.0%
6 28784
 
1.7%
7 23545
 
1.4%
8 21115
 
1.3%
9 18744
 
1.1%
ValueCountFrequency (%)
3205172 1
< 0.1%
3204929 1
< 0.1%
3201035 1
< 0.1%
3197338 1
< 0.1%
3182148 1
< 0.1%
3167394 1
< 0.1%
3163794 1
< 0.1%
3120875 1
< 0.1%
3119450 1
< 0.1%
2758103 1
< 0.1%

min_bank
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5568
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.772303
Minimum0
Maximum313319
Zeros872331
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:36.163723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile125
Maximum313319
Range313319
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1254.9831
Coefficient of variation (CV)23.781094
Kurtosis23549.24
Mean52.772303
Median Absolute Deviation (MAD)0
Skewness131.21265
Sum89072260
Variance1574982.6
MonotonicityNot monotonic
2023-06-12T18:54:36.597224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 872331
51.7%
1 252498
 
15.0%
2 113010
 
6.7%
3 36035
 
2.1%
4 22180
 
1.3%
5 15929
 
0.9%
6 12059
 
0.7%
7 9574
 
0.6%
8 7841
 
0.5%
15 7375
 
0.4%
Other values (5558) 339028
 
20.1%
ValueCountFrequency (%)
0 872331
51.7%
1 252498
 
15.0%
2 113010
 
6.7%
3 36035
 
2.1%
4 22180
 
1.3%
5 15929
 
0.9%
6 12059
 
0.7%
7 9574
 
0.6%
8 7841
 
0.5%
9 6083
 
0.4%
ValueCountFrequency (%)
313319 1
< 0.1%
311423 1
< 0.1%
310427 1
< 0.1%
309667 1
< 0.1%
308055 1
< 0.1%
307627 1
< 0.1%
291059 1
< 0.1%
205786 1
< 0.1%
204803 1
< 0.1%
203734 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1686953 
True
 
907
(Missing)
 
1
ValueCountFrequency (%)
False 1686953
99.9%
True 907
 
0.1%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:37.098026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

pieces_past_due
Real number (ℝ)

SKEWED  ZEROS 

Distinct826
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.043724
Minimum0
Maximum146496
Zeros1662571
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:37.484576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum146496
Range146496
Interquartile range (IQR)0

Descriptive statistics

Standard deviation236.0165
Coefficient of variation (CV)115.48355
Kurtosis207663.23
Mean2.043724
Median Absolute Deviation (MAD)0
Skewness412.3919
Sum3449520
Variance55703.788
MonotonicityNot monotonic
2023-06-12T18:54:38.014323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1662571
98.5%
1 3917
 
0.2%
2 2187
 
0.1%
4 1294
 
0.1%
3 1217
 
0.1%
5 1077
 
0.1%
6 836
 
< 0.1%
12 763
 
< 0.1%
10 744
 
< 0.1%
8 625
 
< 0.1%
Other values (816) 12629
 
0.7%
ValueCountFrequency (%)
0 1662571
98.5%
1 3917
 
0.2%
2 2187
 
0.1%
3 1217
 
0.1%
4 1294
 
0.1%
5 1077
 
0.1%
6 836
 
< 0.1%
7 488
 
< 0.1%
8 625
 
< 0.1%
9 401
 
< 0.1%
ValueCountFrequency (%)
146496 1
< 0.1%
137625 1
< 0.1%
98776 1
< 0.1%
87689 1
< 0.1%
83600 1
< 0.1%
74084 1
< 0.1%
61144 1
< 0.1%
59136 1
< 0.1%
36452 1
< 0.1%
34368 1
< 0.1%

perf_6_month_avg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-6.8720588
Minimum-99
Maximum1
Zeros39013
Zeros (%)2.3%
Negative129478
Negative (%)7.7%
Memory size12.9 MiB
2023-06-12T18:54:38.607530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10.63
median0.82
Q30.97
95-th percentile1
Maximum1
Range100
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation26.556357
Coefficient of variation (CV)-3.864396
Kurtosis8.1173951
Mean-6.8720588
Median Absolute Deviation (MAD)0.15
Skewness-3.1806218
Sum-11599073
Variance705.24009
MonotonicityNot monotonic
2023-06-12T18:54:39.356653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99 143757
 
8.5%
1 132329
 
7.8%
-99 129478
 
7.7%
0.73 106468
 
6.3%
0.98 83611
 
5.0%
0.97 62531
 
3.7%
0.78 46262
 
2.7%
0.95 44152
 
2.6%
0.96 39850
 
2.4%
0.82 39591
 
2.3%
Other values (92) 859831
50.9%
ValueCountFrequency (%)
-99 129478
7.7%
0 39013
 
2.3%
0.01 572
 
< 0.1%
0.02 1053
 
0.1%
0.03 703
 
< 0.1%
0.04 652
 
< 0.1%
0.05 1218
 
0.1%
0.06 1145
 
0.1%
0.07 2303
 
0.1%
0.08 1674
 
0.1%
ValueCountFrequency (%)
1 132329
7.8%
0.99 143757
8.5%
0.98 83611
5.0%
0.97 62531
3.7%
0.96 39850
 
2.4%
0.95 44152
 
2.6%
0.94 36733
 
2.2%
0.93 34815
 
2.1%
0.92 22360
 
1.3%
0.91 31151
 
1.8%

perf_12_month_avg
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-6.4379467
Minimum-99
Maximum1
Zeros32975
Zeros (%)2.0%
Negative122050
Negative (%)7.2%
Memory size12.9 MiB
2023-06-12T18:54:39.990425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10.66
median0.81
Q30.95
95-th percentile0.99
Maximum1
Range100
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation25.843331
Coefficient of variation (CV)-4.0142195
Kurtosis8.9055032
Mean-6.4379467
Median Absolute Deviation (MAD)0.15
Skewness-3.3021812
Sum-10866353
Variance667.87777
MonotonicityNot monotonic
2023-06-12T18:54:40.465653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99 132425
 
7.8%
-99 122050
 
7.2%
0.78 117662
 
7.0%
0.98 92825
 
5.5%
0.97 66029
 
3.9%
0.96 63441
 
3.8%
0.66 53184
 
3.2%
0.9 47174
 
2.8%
0.95 45728
 
2.7%
1 43675
 
2.6%
Other values (92) 903667
53.5%
ValueCountFrequency (%)
-99 122050
7.2%
0 32975
 
2.0%
0.01 2458
 
0.1%
0.02 420
 
< 0.1%
0.03 563
 
< 0.1%
0.04 1026
 
0.1%
0.05 646
 
< 0.1%
0.06 817
 
< 0.1%
0.07 1141
 
0.1%
0.08 1496
 
0.1%
ValueCountFrequency (%)
1 43675
 
2.6%
0.99 132425
7.8%
0.98 92825
5.5%
0.97 66029
3.9%
0.96 63441
3.8%
0.95 45728
 
2.7%
0.94 38752
 
2.3%
0.93 31870
 
1.9%
0.92 30160
 
1.8%
0.91 29820
 
1.8%

local_bo_qty
Real number (ℝ)

SKEWED  ZEROS 

Distinct654
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.62645065
Minimum0
Maximum12530
Zeros1664518
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2023-06-12T18:54:40.921040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12530
Range12530
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.722242
Coefficient of variation (CV)53.830643
Kurtosis38154.955
Mean0.62645065
Median Absolute Deviation (MAD)0
Skewness165.19055
Sum1057361
Variance1137.1896
MonotonicityNot monotonic
2023-06-12T18:54:41.371938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1664518
98.6%
1 7151
 
0.4%
2 2982
 
0.2%
3 1716
 
0.1%
4 1224
 
0.1%
5 979
 
0.1%
6 686
 
< 0.1%
7 552
 
< 0.1%
10 504
 
< 0.1%
8 444
 
< 0.1%
Other values (644) 7104
 
0.4%
ValueCountFrequency (%)
0 1664518
98.6%
1 7151
 
0.4%
2 2982
 
0.2%
3 1716
 
0.1%
4 1224
 
0.1%
5 979
 
0.1%
6 686
 
< 0.1%
7 552
 
< 0.1%
8 444
 
< 0.1%
9 348
 
< 0.1%
ValueCountFrequency (%)
12530 1
< 0.1%
10045 1
< 0.1%
10024 1
< 0.1%
8600 1
< 0.1%
7812 1
< 0.1%
7048 1
< 0.1%
7000 1
< 0.1%
6965 1
< 0.1%
6955 1
< 0.1%
6732 1
< 0.1%

deck_risk
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1300377 
True
387483 
(Missing)
 
1
ValueCountFrequency (%)
False 1300377
77.0%
True 387483
 
23.0%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:41.860137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1687615 
True
 
245
(Missing)
 
1
ValueCountFrequency (%)
False 1687615
> 99.9%
True 245
 
< 0.1%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:42.254154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

ppap_risk
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1484026 
True
203834 
(Missing)
 
1
ValueCountFrequency (%)
False 1484026
87.9%
True 203834
 
12.1%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:42.677601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
True
1626774 
False
 
61086
(Missing)
 
1
ValueCountFrequency (%)
True 1626774
96.4%
False 61086
 
3.6%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:43.124701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

rev_stop
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1687129 
True
 
731
(Missing)
 
1
ValueCountFrequency (%)
False 1687129
> 99.9%
True 731
 
< 0.1%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:43.587607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1676567 
True
 
11293
(Missing)
 
1
ValueCountFrequency (%)
False 1676567
99.3%
True 11293
 
0.7%
(Missing) 1
 
< 0.1%
2023-06-12T18:54:44.084454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-06-12T18:53:21.778406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:48.213190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:02.292015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:14.930159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:28.069094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:45.753468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:03.816181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:20.397115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:35.866105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:51.909903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:06.355346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:19.687950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:34.712319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:54.096995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:07.581788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:22.653729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:49.204061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:03.069528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:15.711612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:29.133870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:47.033859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:04.881349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:21.482095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:36.823210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:52.915814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:07.207736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:20.569903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:35.929667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:54.978936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:08.516173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:23.595354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:50.122571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:03.893373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:16.461406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:30.207747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:48.699341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:05.855663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:22.625553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:37.787939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:53.781998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:08.060628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:21.382454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:38.375436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:55.841504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:09.475182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:24.559359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:50.934126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:04.781556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:17.202788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:31.223352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:49.947341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:06.877486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:23.794665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:38.830445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:54.638906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:09.008545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:22.293047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:40.649601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:56.654227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:10.454326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:25.736355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:51.892400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:05.652368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:18.040432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:32.262166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:51.056900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:07.974530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:25.052864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:39.899436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:55.726318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:09.915329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:23.268625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:41.737939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:57.500020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:11.354049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:26.560615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:52.714060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:06.403816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:18.909485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:33.042920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:52.222164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:09.098084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:26.151403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:41.039269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:56.563780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:10.734926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:24.236500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:43.846497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:58.388667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:12.261219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:27.464993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:53.592904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:07.245236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:19.781431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:33.874577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:53.354841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:10.196123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:27.307469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:41.992933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:57.442588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:11.585963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:25.143301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:45.116372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:59.324655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:13.364952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:28.576344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:54.513734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:08.134618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:20.620576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:34.869631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:54.588315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:11.245290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:28.427114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:42.854461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:58.448476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:12.467330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:25.982883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:46.150917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:00.227904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:14.376545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:29.497564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:56.282741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:09.005224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:21.344593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:35.663632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:55.758865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:12.276140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:29.403519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:43.759811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:00.217705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:13.469607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:26.883950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:47.025924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:01.203277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:15.328910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:30.370695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:57.092187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:09.841873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:22.139683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:36.888291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:57.135456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:13.325408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:30.334043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:44.775515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:01.116028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:14.340979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:27.717321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:48.113631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:02.104641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:16.244224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:31.281762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:57.940321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:10.683785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:22.936365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:38.223439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:58.217752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:14.396767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:31.173367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:45.661173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:02.153915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:15.257325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:28.783127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:49.144191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:03.002671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:17.152231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:32.472810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:58.794116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:11.456084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:23.927187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:39.964835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:59.279843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:15.484843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:32.079510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:46.946034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:02.941818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:16.099844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:29.719673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:50.468109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:03.996961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:18.015536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:33.497330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:49:59.718799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:12.361029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:24.973318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:41.519499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:00.490211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:16.584436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:32.990300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:48.427202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:03.882740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:16.968296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:31.114458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:51.423568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:04.972935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:19.001053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:34.386827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:00.631326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:13.189256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:25.937911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:43.012228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:01.717893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:18.081896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:33.983618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:49.578829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:04.730246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:17.769861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:32.390122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:52.326957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:05.787632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:19.966641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:35.215375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:01.480530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:14.058394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:27.032250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:50:44.460646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:02.718952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:19.233124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:34.930998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:51:50.753627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:05.529181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:18.693755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:33.557338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:52:53.211819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:06.665239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T18:53:20.875331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T18:54:44.600592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
national_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtypotential_issuedeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
national_inv1.000-0.0420.3760.1490.1860.2040.5410.5560.5700.5790.5120.0130.1250.1170.0290.0000.0020.0000.0050.0000.0000.000
lead_time-0.0421.000-0.059-0.122-0.131-0.131-0.084-0.086-0.085-0.0810.013-0.009-0.071-0.080-0.0110.0060.3660.0050.0290.3730.0100.018
in_transit_qty0.376-0.0591.0000.5430.5720.5800.6030.5870.5740.5660.5520.1220.1220.1210.1710.0130.0040.0000.0000.0040.0000.000
forecast_3_month0.149-0.1220.5431.0000.9100.8700.5520.5460.5250.5110.4200.1970.1270.1240.1840.0000.0050.0000.0020.0000.0000.000
forecast_6_month0.186-0.1310.5720.9101.0000.9580.6090.6120.5930.5790.4530.1790.1600.1570.1760.0000.0050.0000.0020.0000.0000.000
forecast_9_month0.204-0.1310.5800.8700.9581.0000.6330.6430.6280.6140.4670.1700.1780.1750.1710.0000.0050.0000.0030.0000.0000.000
sales_1_month0.541-0.0840.6030.5520.6090.6331.0000.9150.8850.8730.6380.1050.1850.1810.1670.0000.0030.0000.0030.0060.0000.000
sales_3_month0.556-0.0860.5870.5460.6120.6430.9151.0000.9630.9470.6400.1040.1960.1910.1640.0000.0060.0000.0020.0040.0000.000
sales_6_month0.570-0.0850.5740.5250.5930.6280.8850.9631.0000.9830.6400.1000.2010.1950.1590.0000.0060.0000.0020.0040.0000.000
sales_9_month0.579-0.0810.5660.5110.5790.6140.8730.9470.9831.0000.6410.0960.2030.1970.1560.0000.0060.0000.0020.0030.0000.000
min_bank0.5120.0130.5520.4200.4530.4670.6380.6400.6400.6411.0000.0910.1090.1050.1540.0090.0060.0000.0060.0100.0000.000
pieces_past_due0.013-0.0090.1220.1970.1790.1700.1050.1040.1000.0960.0911.000-0.052-0.0520.0940.0000.0010.0000.0000.0000.0000.000
perf_6_month_avg0.125-0.0710.1220.1270.1600.1780.1850.1960.2010.2030.109-0.0521.0000.929-0.0020.0040.2320.0030.0390.2500.0330.012
perf_12_month_avg0.117-0.0800.1210.1240.1570.1750.1810.1910.1950.1970.105-0.0520.9291.000-0.0030.0040.2370.0030.0380.2440.0340.012
local_bo_qty0.029-0.0110.1710.1840.1760.1710.1670.1640.1590.1560.1540.094-0.002-0.0031.0000.0000.0030.0130.0010.0010.0000.009
potential_issue0.0000.0060.0130.0000.0000.0000.0000.0000.0000.0000.0090.0000.0040.0040.0001.0000.0020.0130.0090.0020.0000.014
deck_risk0.0020.3660.0040.0050.0050.0050.0030.0060.0060.0060.0060.0010.2320.2370.0030.0021.0000.0000.0370.1400.0090.012
oe_constraint0.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0030.0130.0130.0001.0000.0040.0020.0000.003
ppap_risk0.0050.0290.0000.0020.0020.0030.0030.0020.0020.0020.0060.0000.0390.0380.0010.0090.0370.0041.0000.0330.0200.009
stop_auto_buy0.0000.3730.0040.0000.0000.0000.0060.0040.0040.0030.0100.0000.2500.2440.0010.0020.1400.0020.0331.0000.0500.002
rev_stop0.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0340.0000.0000.0090.0000.0200.0501.0000.001
went_on_backorder0.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0120.0090.0140.0120.0030.0090.0020.0011.000

Missing values

2023-06-12T18:53:39.698233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T18:53:51.017736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-12T18:54:17.391322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
010268270.0NaN0.00.00.00.00.00.00.00.00.0No0.0-99.00-99.000.0NoNoNoYesNoNo
110433842.09.00.00.00.00.00.00.00.00.00.0No0.00.990.990.0NoNoNoYesNoNo
210436962.0NaN0.00.00.00.00.00.00.00.00.0No0.0-99.00-99.000.0YesNoNoYesNoNo
310438527.08.00.00.00.00.00.00.00.00.01.0No0.00.100.130.0NoNoNoYesNoNo
410440488.0NaN0.00.00.00.00.00.00.04.02.0No0.0-99.00-99.000.0YesNoNoYesNoNo
5104419813.08.00.00.00.00.00.00.00.00.00.0No0.00.820.870.0NoNoNoYesNoNo
610446431095.0NaN0.00.00.00.00.00.00.00.04.0No0.0-99.00-99.000.0YesNoNoYesNoNo
710450986.02.00.00.00.00.00.00.00.00.00.0No0.00.000.000.0YesNoYesYesNoNo
81045815140.0NaN0.015.0114.0152.00.00.00.00.00.0No0.0-99.00-99.000.0NoNoNoYesNoNo
910458674.08.00.00.00.00.00.00.00.00.00.0No0.00.820.870.0NoNoNoYesNoNo
skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
16878511373539-6.09.036.0130.0130.0130.00.00.054.057.03.0No0.00.030.1042.0NoNoNoYesNoNo
168785214786832.08.00.0966.0966.01116.047.0512.01361.02060.0455.0No0.00.840.7746.0NoNoNoYesNoNo
168785314899200.02.00.02071.03025.03412.04.0764.0764.0765.0657.0No0.00.980.994.0NoNoNoNoNoYes
16878541392420124.08.0140.0410.0780.01240.0128.0464.0849.01074.0111.0No0.00.850.901.0NoNoNoYesNoNo
168785514077540.02.00.010.010.010.00.05.07.07.00.0No0.00.690.695.0YesNoNoYesNoNo
16878561373987-1.0NaN0.05.07.09.01.03.03.08.00.0No0.0-99.00-99.001.0NoNoNoYesNoNo
16878571524346-1.09.00.07.09.011.00.08.011.012.00.0No0.00.860.841.0YesNoNoNoNoYes
1687858143956362.09.016.039.087.0126.035.063.0153.0205.012.0No0.00.860.846.0NoNoNoYesNoNo
1687859150200919.04.00.00.00.00.02.07.012.020.01.0No0.00.730.781.0NoNoNoYesNoNo
1687860(1687860 rows)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

national_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder# duplicates
124000.0NaN0.00.00.00.00.00.00.00.00.0No0.0-99.00-99.000.0NoNoNoYesNoNo6921
277982.012.00.00.00.00.00.00.00.00.00.0No0.00.780.780.0NoNoNoYesNoNo3140
216992.04.00.00.00.00.00.00.00.00.00.0No0.00.730.780.0NoNoNoYesNoNo2690
9155913.012.00.00.00.00.00.00.00.00.01.0No0.00.480.480.0YesNoNoYesNoNo2683
277572.012.00.00.00.00.00.00.00.00.00.0No0.00.630.720.0NoNoNoYesNoNo2534
8025410.012.00.00.00.00.00.00.00.00.01.0No0.00.480.480.0YesNoNoYesNoNo2253
375043.012.00.00.00.00.00.00.00.00.00.0No0.00.630.720.0NoNoNoYesNoNo2169
277782.012.00.00.00.00.00.00.00.00.00.0No0.00.730.790.0NoNoNoYesNoNo2163
9156213.012.00.00.00.00.00.00.00.00.01.0No0.00.580.580.0YesNoNoYesNoNo2137
266932.09.00.00.00.00.00.00.00.00.00.0No0.00.700.660.0NoNoNoYesNoNo2082